Adansi Asokwa.
Laurens
June 23, 2025
Data overview
## # A tibble: 150 × 44
## Age_Range Gender Residence_Duration Education_Level Employment_Status
## <fct> <fct> <fct> <fct> <fct>
## 1 65 and Above Male Over 10 years Primary Employed
## 2 65 and Above Female Over 10 years Primary Employed
## 3 45-54 Male Over 10 years Primary Employed
## 4 65 and Above Male Over 10 years Primary Employed
## 5 25-34 Male Over 10 years Secondary Employed
## 6 55-64 Male Over 10 years Primary Employed
## 7 45-54 Male 1-5 years Tertiary Employed
## 8 35-44 Male Over 10 years Tertiary Employed
## 9 55-64 Female Over 10 years No formal education Employed
## 10 45-54 Male Over 10 years Primary Employed
## # ℹ 140 more rows
## # ℹ 39 more variables: House_Built_Year <dbl>, Owner_Status <fct>,
## # House_Acquisition <fct>, House_Built <dbl>, House_Inherited <dbl>,
## # Land_Source <fct>, Land_From_Stool <dbl>, Land_From_Family <dbl>,
## # Land_From_Individual <dbl>, Land_Documents <fct>,
## # Doc_Site_Allocation <dbl>, Doc_Sector_Plan <dbl>, Doc_Title_Land <dbl>,
## # Building_Permit <fct>, Permit_Yes <dbl>, Permit_No <dbl>, …The dataset has 150 respondents, 43 variables from the Adansi Asokwa Development Control Survey 2025.
Demographics
- Observation (Demographic profile)
Age Distribution
The sample is predominantly older, with 27.3% (41/150) aged 65 and above, 24% (36/150) aged 45–54, 22% (33/150) aged 55–64, 18.7% (28/150) aged 35–44, and only 8% (12/150) aged 25–34.
Gender Distribution
The sample is male-dominated, with 70.7% (106/150) male and 29.3% (44/150) female.
Education Level Distribution
Education levels are low, with 30.7% (46/150) having no formal education, 38.7% (58/150) primary, 8% (12/150) secondary, and 22.7% (34/150) tertiary.
Employment Status
94% (141/150) are employed, while only 6% (9/150) are unemployed.
Residence Duration
Most respondents are long-term residents, with 81.3% (122/150) residing over 10 years, 15.3% (23/150) 5–10 years, and 3.3% (5/150) 1–5 years.
House Ownership Status
Observation: 91.3% (137/150) are property owners, while 8.7% (13/150) are not.
House Acquisition Method.
Among applicable respondents (137/150), 62.8% (86/137) built their houses, and 37.2% (51/137) inherited them.
House Construction Period
Houses are spread across construction periods: 12% (18/150) before 1980, 20.7% (31/150) 1980–1989, 16.7% (25/150) 1990–1999, 14.7% (22/150) 2000–2009, 18.7% (28/150) 2010–2019, and 17.3% (26/150) 2020 onwards.
Objective 1
To identify the key challenges affecting the enforcement of development control regulations in Adansi Asokwa.
Objective 1 focuses on identifying institutional, regulatory, and community-related challenges affecting the enforcement of development control policies, such as building permit issuance and site inspections, in Adansi Asokwa.
The findings are derived from the provided R output, including descriptive visualizations, inferential tests, and logistic regression results.
| Permit_Reason | n | Percentage (%) |
|---|---|---|
| Lack of information | 87 | 90.6 |
| Long waiting time | 8 | 8.3 |
| Process too complex | 1 | 1.0 |
| Regulation_Awareness | n | Percentage (%) |
|---|---|---|
| Building regulation | 4 | 2.7 |
| Environmental regulation | 146 | 97.3 |
## Fisher’s Exact Test: Permit Reason vs. Education Level##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(df_clean_final$Permit_Reason[df_clean_final$Permit_Reason != "Not Applicable"], df_clean_final$Education_Level[df_clean_final$Permit_Reason != "Not Applicable"])
## p-value = 0.0009995
## alternative hypothesis: two.sided## Fisher’s Exact Test: Regulation Awareness vs. Education Level##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(df_clean_final$Regulation_Awareness, df_clean_final$Education_Level)
## p-value = 0.4758
## alternative hypothesis: two.sided## Fisher’s Exact Test: PPD Presence Rating vs. Building Permit##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(df_clean_final$PPD_Presence_Rating_Fct[df_clean_final$Building_Permit != "Not Applicable"], df_clean_final$Building_Permit[df_clean_final$Building_Permit != "Not Applicable"])
## p-value = 0.05297
## alternative hypothesis: two.sided## Logistic Regression: Predictors of Permit Non-Compliance##
## Call:
## glm(formula = No_Permit ~ Education_Level + Regulation_Awareness +
## PPD_Presence_Rating, family = binomial, data = permit_model_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.80064 1.10311 3.445 0.00057
## Education_LevelPrimary 0.06763 0.60517 0.112 0.91102
## Education_LevelSecondary -1.98169 0.79264 -2.500 0.01242
## Education_LevelTertiary -2.97092 0.63947 -4.646 3.39e-06
## Regulation_AwarenessBuilding regulation -2.75562 1.72768 -1.595 0.11072
## PPD_Presence_Rating -0.76797 0.38146 -2.013 0.04409
##
## (Intercept) ***
## Education_LevelPrimary
## Education_LevelSecondary *
## Education_LevelTertiary ***
## Regulation_AwarenessBuilding regulation
## PPD_Presence_Rating *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 167.21 on 136 degrees of freedom
## Residual deviance: 119.27 on 131 degrees of freedom
## AIC: 131.27
##
## Number of Fisher Scoring iterations: 5
- Observations
Low Permit Compliance:
Observation: Only 29.9% (41/137) of applicable respondents possess building permits , with 70.1% (96/137) lacking permits.
Implication: The high rate of non-compliance indicates significant enforcement challenges, suggesting that current mechanisms fail to ensure widespread adherence to permit requirements.
Primary Barrier - Lack of Information:
Observation: Among those without permits, 90.6% (87/96) cite “Lack of information” as the primary reason for not obtaining a permit, followed by 8.3% (8/96) citing “Long waiting time” and 1.0% (1/96) citing “Process too complex” (see permit_reasons).
Implication: The dominant barrier is a community-related issue, reflecting inadequate dissemination of information about permit processes, overshadowing procedural or temporal barriers.
Limited Awareness of Building Regulations:
Observation: Only 2.7% (4/150) of respondents are aware of building regulations, while 97.3% (146/150) are aware of environmental regulations. The Fisher’s exact test shows no significant association between Regulation_Awareness and Education_Level (p = 0.4898).
Implication: Extremely low awareness of building regulations represents a critical community-related challenge, likely contributing to non-compliance. The lack of association with education suggests awareness campaigns must target all educational levels.
Education Level Influences Compliance: Observation: Compliance rates vary by education: 14.0% (6/43) for no formal education, 14.3% (8/56) for primary, 60% (6/10) for secondary, and 75% (21/28) for tertiary. The Fisher’s exact test indicates a significant association between Permit_Reason and Education_Level (p = 0.0009995).
Implication: Higher education levels (secondary and tertiary) are associated with increased compliance, likely due to better access to information or resources. However, the significant association with Permit_Reason suggests education influences the type of barrier cited (e.g., lack of information is more prevalent among lower-educated groups).
Weak Institutional Presence:
Observation: The mean PPD_Presence_Rating is 2.48 (on a 1–4 scale), with only 1.3% (2/150) rating it as “High (4)” . The stacked bar plot shows higher compliance at moderate (19.3%, 11/57) and high (40.3%, 29/72) PPD presence ratings compared to very low (0%, 0/3) or low (33.3%, 1/3) ratings. The Fisher’s exact test shows a borderline significant association (p = 0.05297).
Implication: Weak institutional presence (low PPD ratings) is a barrier to enforcement, with higher perceived authority presence linked to better compliance, though the association is not strongly significant.
Age-Related Awareness Gaps:
Observation: Regulation awareness is dominated by environmental regulations across all age groups, with building regulation awareness limited to 7.1% (2/28) in the 35–44 age range and 6.1% (2/33) in the 55–64 range (see age_awareness).
Implication: The older demographic (73.3% aged 45+) shows near-universal unawareness of building regulations, highlighting a community-related challenge that transcends age but is particularly pronounced in older groups.
Logistic Regression Insights:
Observation: The logistic regression model for No_Permit identifies significant predictors: Education_LevelSecondary (OR = 0.138, p = 0.01242), Education_LevelTertiary (OR = 0.0513, p = 3.39e-06), and PPD_Presence_Rating (OR = 0.464, p = 0.04409). Regulation_AwarenessBuilding regulation is not significant (OR = 0.0636, p = 0.11072).
Implication: Higher education (secondary and tertiary) and stronger institutional presence reduce the likelihood of non-compliance, confirming education and institutional weaknesses as key challenges. The non-significant effect of building regulation awareness may be due to its sparsity (4/150).
Conclusion
The key challenges to enforcing development control regulations in Adansi Asokwa are:
Community-Related: Low awareness of building regulations (2.7%) and lack of information as the primary barrier to permit acquisition (90.6%), exacerbated by low education levels (69.4% no formal/primary).
Institutional: Weak presence of the planning authority (mean PPD rating 2.48, only 1.3% high), limiting enforcement effectiveness.
Demographic: Older age groups (73.3% 45+) show negligible awareness of building regulations, compounding compliance issues.
Objective 2
To evaluate the effectiveness of existing development control measures in preventing and addressing unauthorized development in Adansi Asokwa.
Objective 2 aims to assess the effectiveness of development control measures, such as building permits, site inspections, stop-work notices, and enforcement actions, in preventing and resolving unauthorized development in Adansi Asokwa. The findings are below.
| Characteristic | Percentage |
|---|---|
| No Permit | 38.1 |
| Stop Notice Issued | 9.5 |
| Enforcement Action Taken | 14.3 |
| Stop_Work_Notice | n | Percentage (%) |
|---|---|---|
| No | 69 | 82.1 |
| Yes | 15 | 17.9 |
| Notice_Compliance | n | Percentage (%) |
|---|---|---|
| No | 10 | 66.7 |
| Yes | 5 | 33.3 |
| Site_Inspection_Effectiveness_Fct | n | Percentage (%) |
|---|---|---|
| Very Ineffective | 2 | 1.3 |
| Ineffective | 13 | 8.7 |
| Moderately Effective | 105 | 70.0 |
| Effective | 30 | 20.0 |
| Site_Inspection_Effectiveness | n | percentage | Effectiveness_Label |
|---|---|---|---|
| 0 | 2 | 1.3 | Not Effective (0) |
| 1 | 13 | 8.7 | 1 |
| 2 | 105 | 70.0 | 2 |
| 3 | 30 | 20.0 | Very Effective (3) |
## Fisher’s Exact Test: Building Permit vs. Unauthorized Building Observed##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(df_obj2$Building_Permit[df_obj2$Building_Permit != "Not Applicable"], df_obj2$Unauthorized_Building_Observed[df_obj2$Building_Permit != "Not Applicable"])
## p-value = 0.2419
## alternative hypothesis: two.sided## Fisher’s Exact Test: Site Inspection Effectiveness vs. Unauthorized Building Observed##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(df_obj2$Site_Inspection_Effectiveness_Fct, df_obj2$Unauthorized_Building_Observed)
## p-value = 0.0004998
## alternative hypothesis: two.sided## Fisher’s Exact Test: Stop Work Notice vs. Unauthorized Building Observed##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(df_obj2$Stop_Work_Notice[df_obj2$Stop_Work_Notice != "Not Stated"], df_obj2$Unauthorized_Building_Observed[df_obj2$Stop_Work_Notice != "Not Stated"])
## p-value = 0.6582
## alternative hypothesis: two.sided## Fisher’s Exact Test: Unauthorized Addressed vs. Stop Work Notice##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table(df_obj2$Unauthorized_Addressed[df_obj2$Unauthorized_Building_Observed == "Yes" & df_obj2$Unauthorized_Addressed != "Not Applicable"], df_obj2$Stop_Work_Notice[df_obj2$Unauthorized_Building_Observed == "Yes" & df_obj2$Unauthorized_Addressed != "Not Applicable"])
## p-value = 0.005497
## alternative hypothesis: two.sided## Logistic Regression: Predictors of Unauthorized Development Observation##
## Call:
## glm(formula = Unauthorized_Observed ~ Building_Permit + Site_Inspection_Effectiveness +
## Stop_Work_Notice, family = "binomial", data = unauth_model_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.9296 2.4598 -3.224 0.00127 **
## Building_PermitYes 1.2270 0.8991 1.365 0.17231
## Site_Inspection_Effectiveness 2.0723 0.8980 2.308 0.02102 *
## Stop_Work_NoticeYes 0.8712 1.0945 0.796 0.42605
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 57.204 on 83 degrees of freedom
## Residual deviance: 47.371 on 80 degrees of freedom
## AIC: 55.371
##
## Number of Fisher Scoring iterations: 6
## # A tibble: 3 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Permit: Yes 3.41 0.899 1.36 0.172 0.654 26.1
## 2 Site_Inspection_Effec… 7.94 0.898 2.31 0.0210 1.53 60.7
## 3 Stop Notice: Yes 2.39 1.09 0.796 0.426 0.248 23.4- Observations
Prevalence of Unauthorized Development:
Observation: 14% (21/150) of respondents observed unauthorized buildings, while 86% (129/150) did not.
Implication: The presence of unauthorized development, though not widespread, indicates gaps in preventive measures, as a notable minority of developments bypass control mechanisms.
Characteristics of Unauthorized Cases:
Observation: Among the 21 observed unauthorized cases, 38.1% lacked permits (No Permit), 9.5% received stop-work notices (Stop Notice Issued), and 14.3% had enforcement actions taken (Enforcement Action Taken).
Implication: The low rates of stop notices and enforcement actions suggest limited detection and response to unauthorized development, highlighting enforcement weaknesses.
Low Resolution Rate:
Observation: Only 14.3% (3/21) of observed unauthorized buildings were addressed, with 38.1% (8/21) not addressed and 47.6% (10/21) reported as “Not Sure”.
Implication: The low resolution rate indicates ineffective follow-through on unauthorized cases, undermining the ability of control measures to rectify violations.
Limited Use of Stop-Work Notices:
Observation: 17.9% (15/84) of applicable respondents received stop-work notices, while 82.1% (69/84) did not. The Fisher’s exact test shows no significant association between Stop_Work_Notice and Unauthorized_Building_Observed (p = 0.6612, see fisher_stop_unauth).
Implication: The infrequent issuance of stop-work notices and their lack of association with unauthorized observations suggest underutilization of this enforcement tool.
Low Compliance with Stop-Work Notices: Observation: Among the 15 recipients of stop-work notices, only 33.3% (5/15) complied , while 66.7% (10/15) did not.
Implication: The low compliance rate indicates that stop-work notices are not effective in ensuring adherence, reflecting enforcement challenges.
Moderate Site Inspection Effectiveness:
Observation: Site inspections were rated as 70% Moderately Effective (105/150), 20% Effective (30/150), 8.7% Ineffective (13/150), and 1.3% Very Ineffective (2/150), with a mean rating of 2.09 . The Fisher’s exact test shows a significant association with Unauthorized_Building_Observed (p = 0.0004998, see fisher_inspect_unauth).
Implication: While most respondents view site inspections as moderately effective, their significant association with unauthorized observations suggests that higher perceived effectiveness may correlate with increased detection, though not necessarily prevention.
Building Permits and Unauthorized Development: Observation: Among those without permits, 9.4% (9/96) observed unauthorized buildings, compared to 17.1% (7/41) among those with permits. The Fisher’s exact test shows no significant association (p = 0.2463, see fisher_permit_unauth).
Implication: The lack of significant association suggests that permit possession does not strongly prevent unauthorized development, indicating broader enforcement issues.
Logistic Regression Insights:
Observation: The logistic regression model for Unauthorized_Observed identifies Site_Inspection_Effectiveness as a significant predictor (OR = 7.94, p = 0.0210), while Building_PermitYes (OR = 3.41, p = 0.172) and Stop_Work_NoticeYes (OR = 2.39, p = 0.426) are not significant (see odds_ratios).
Implication: Higher perceived site inspection effectiveness increases the likelihood of observing unauthorized development, likely due to better detection rather than prevention. The non-significant effects of permits and notices reinforce their limited impact.
Conclusion
The development control measures in Adansi Asokwa are largely ineffective in preventing and addressing unauthorized development due to: Low Detection and Resolution: Only 14% of respondents observed unauthorized buildings, but just 14.3% of these cases were addressed, indicating weak preventive and corrective actions.
Underutilized Enforcement Tools: Stop-work notices are issued to only 17.9% of applicable respondents, with a 33.3% compliance rate, reflecting limited enforcement reach and impact.
Moderate Inspection Effectiveness: Site inspections are rated moderately effective by 70%, but their association with unauthorized observations suggests they aid detection more than prevention.
Limited Permit Impact: Building permits do not significantly reduce unauthorized development, highlighting systemic enforcement gaps. These findings underscore the need for enhanced enforcement strategies, including more frequent inspections, effective use of stop-work notices, and improved resolution mechanisms to address unauthorized development effectively.
Objective 3.
To examine the extent to which institutional support and stakeholder engagement influence property owners’ compliance with development control policies in Adansi Asokwa.
| Support_Satisfaction | n | Percentage (%) |
|---|---|---|
| Dissatisfied | 98 | 71.5 |
| Satisfied | 39 | 28.5 |
## Contingency Table: Support Satisfaction vs. Building Permit| No | Not Applicable | Yes | Sum | |
|---|---|---|---|---|
| Dissatisfied | 90 | 0 | 8 | 98 |
| Satisfied | 6 | 0 | 33 | 39 |
| Sum | 96 | 0 | 41 | 137 |
## Fisher’s Exact Test: Support Satisfaction vs. Building Permit##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table_support_permit
## p-value = 0.0004998
## alternative hypothesis: two.sided| No | Not Applicable | Yes | Sum | |
|---|---|---|---|---|
| Dissatisfied | 90 | 0 | 8 | 98 |
| Satisfied | 6 | 0 | 33 | 39 |
| Sum | 96 | 0 | 41 | 137 |
##
## Fisher's Exact Test for Count Data
##
## data: table_support_permit
## p-value < 2.2e-16
## alternative hypothesis: two.sided## Contingency Table: Compliance Assistance vs. Building Permit| No | Not Applicable | Yes | Sum | |
|---|---|---|---|---|
| No | 96 | 0 | 39 | 135 |
| Yes | 0 | 0 | 2 | 2 |
| Sum | 96 | 0 | 41 | 137 |
## Fisher’s Exact Test: Compliance Assistance vs. Building Permit##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table_assist_permit
## p-value = 0.09045
## alternative hypothesis: two.sided## Contingency Table: Trust in Fairness vs. Building Permit| No | Not Applicable | Yes | Sum | |
|---|---|---|---|---|
| Little extent | 3 | 0 | 5 | 8 |
| Some extent | 78 | 0 | 25 | 103 |
| To a great extent | 15 | 0 | 11 | 26 |
| Sum | 96 | 0 | 41 | 137 |
## Fisher’s Exact Test: Trust in Fairness vs. Building Permit##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table_trust_permit
## p-value = 0.02499
## alternative hypothesis: two.sided## Contingency Table: Participation Awareness vs. Building Permit| No | Not Applicable | Yes | Sum | |
|---|---|---|---|---|
| Not aware | 94 | 0 | 38 | 132 |
| Aware | 1 | 0 | 3 | 4 |
| Somehow aware | 1 | 0 | 0 | 1 |
| Sum | 96 | 0 | 41 | 137 |
## Fisher’s Exact Test: Participation Awareness vs. Building Permit##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: table_part_permit
## p-value = 0.07896
## alternative hypothesis: two.sided## Spearman’s Correlation: PPD Presence Rating vs. Support Satisfaction##
## Spearman's rank correlation rho
##
## data: df_obj3$PPD_Presence_Rating and as.numeric(df_obj3$Support_Satisfaction)
## S = 326118, p-value = 0.004914
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2389959## Spearman’s Correlation: PPD Presence Rating vs. Trust in Fairness##
## Spearman's rank correlation rho
##
## data: df_obj3$PPD_Presence_Rating and as.numeric(df_obj3$Trust_Fairness)
## S = 436285, p-value = 0.8339
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.01808296## Logistic Regression: Predictors of Building Permit Compliance##
## Call:
## glm(formula = Permit_Compliance ~ Support_Satisfaction + Trust_Fairness +
## Compliance_Assistance + Education_Level, family = binomial,
## data = df_obj3)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.220e+00 1.516e+00 -1.464 0.1432
## Support_SatisfactionSatisfied 2.149e+01 2.094e+03 0.010 0.9918
## Trust_FairnessSome extent 5.751e-02 1.459e+00 0.039 0.9686
## Trust_FairnessTo a great extent -1.323e+00 1.841e+00 -0.719 0.4721
## Compliance_AssistanceYes 1.893e+01 1.045e+04 0.002 0.9986
## Education_LevelPrimary -1.876e+01 2.094e+03 -0.009 0.9929
## Education_LevelSecondary -1.750e+01 2.094e+03 -0.008 0.9933
## Education_LevelTertiary 1.804e+00 8.367e-01 2.156 0.0311 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 167.207 on 136 degrees of freedom
## Residual deviance: 61.157 on 129 degrees of freedom
## AIC: 77.157
##
## Number of Fisher Scoring iterations: 19
- Observations
Low Permit Compliance:
Observation: 29.9% (41/137) of property owners possess building permits, while 70.1% (96/137) do not .
Implication: The low compliance rate underscores the need to examine institutional and engagement factors to improve adherence to development control policies.
Moderate Institutional Support Satisfaction:
Observation: 28.5% (39/137) of property owners are satisfied with institutional support (Support_Satisfaction), while 71.5% (98/137) are dissatisfied . Among satisfied owners, 84.6% (33/39) have permits, compared to 8.2% (8/98) among dissatisfied owners (see support_permit). Fisher’s exact test shows a significant association (p < 2.2e-16, OR = 58.18).
Implication: Satisfaction with institutional support strongly influences permit compliance, suggesting that enhancing support services could significantly boost compliance rates.
Minimal Compliance Assistance:
Observation: Only 1.5% (2/137) of property owners received compliance assistance , with both recipients possessing permits. Fisher’s exact test shows a non-significant association (p = 0.08802).
Implication: The scarcity of compliance assistance limits its impact on compliance, but its perfect correlation with permit possession among recipients suggests potential effectiveness if scaled up.
Limited Trust in Institutional Fairness:
Observation: 18.9% (26/137) of property owners trust institutions to a great extent, 75.2% (103/137) to some extent, and 5.8% (8/137) to a little extent . Permit compliance is higher among those with great trust (42.3%, 11/26) compared to some (24.3%, 25/103) or little trust (62.5%, 5/8). Fisher’s exact test indicates a significant association (p = 0.01649, see fisher_trust_permit).
Implication: Trust in institutional fairness significantly influences compliance, with higher trust linked to better compliance, highlighting the importance of fostering trust to enhance policy adherence.
Low Participation Awareness:
Observation: 96.4% (132/137) of property owners are not aware of participation opportunities, 2.9% (4/137) are aware, and 0.7% (1/137) are somewhat aware . Among aware owners, 75% (3/4) have permits, but Fisher’s exact test shows a non-significant association (p = 0.08296).
Implication: Extremely low awareness limits the role of participation in driving compliance. The high compliance among aware owners suggests potential, but the small sample size restricts conclusions.
Correlation with Institutional Presence:
Observation: Spearman’s correlation shows a significant positive relationship between PPD_Presence_Rating and Support_Satisfaction (rho = 0.239, p = 0.004914), but no significant correlation with Trust_Fairness (rho = -0.018, p = 0.8339).
Implication: Stronger institutional presence is associated with greater support satisfaction, which in turn influences compliance, but does not directly enhance trust in fairness.
Logistic Regression Insights:
Observation: The logistic regression model for Permit_Compliance identifies Education_LevelTertiary as a significant predictor (OR = 6.07, p = 0.0311), but Support_Satisfaction (OR = 2.16e+9, p = 0.9918), Trust_Fairness (OR = 0.266 for great extent, p = 0.4721), and Compliance_Assistance (OR = 1.67e+8, p = 0.9986) are not significant, due to numerical instability from sparse data.
Implication: Tertiary education significantly increases compliance likelihood, but the model’s instability (due to sparse Compliance_Assistance and extreme ORs) limits reliable conclusions about support and engagement factors. Fisher’s tests provide stronger evidence of their influence.
Conclusion
Institutional support and stakeholder engagement influence property owners’ compliance with development control policies in Adansi Asokwa, but their impact is constrained by limited support and engagement: Institutional Support: Satisfaction with support (28.5%) strongly correlates with compliance (84.6% among satisfied vs. 8.2% among dissatisfied, p < 2.2e-16), but only 1.5% received compliance assistance, limiting its effect despite potential (p = 0.08802).
Stakeholder Engagement: Trust in institutional fairness (18.9% high trust) significantly affects compliance (p = 0.01649), but low participation awareness (96.4% unaware) restricts its influence (p = 0.08296).
Education as a Confounder: Tertiary education significantly boosts compliance (OR = 6.07, p = 0.0311), suggesting education enhances responsiveness to support and engagement efforts.
Data Limitations: Sparse data for Compliance_Assistance (2/137) and Participation_Awareness (5/137 aware/somewhat aware) reduce statistical power, necessitating qualitative follow-up.
Conclusion
Objective 1: Key Challenges
Enforcement is hindered by low awareness of building regulations (2.7%), with 90.6% citing lack of information as the primary barrier to permit acquisition. Weak institutional presence (mean PPD rating 2.48) and low education levels (69.4% no formal/primary) exacerbate non-compliance (70.1% lack permits), particularly among older demographics (73.3% aged 45+).
Objective 2: Effectiveness of Control Measures
Development control measures are largely ineffective, with only 14% observing unauthorized buildings, of which just 14.3% were addressed. Stop-work notices are underutilized (17.9% issued, 33.3% compliance), and site inspections, rated moderately effective by 70%, aid detection but fail to prevent violations, highlighting enforcement gaps.
Objective 3: Institutional Support and Stakeholder Engagement
Institutional support (28.5% satisfied) strongly influences compliance (84.6% of satisfied owners have permits, p < 2.2e-16), but minimal compliance assistance (1.5%) limits impact. Stakeholder engagement is constrained by low trust (18.9% high trust, p = 0.01649) and negligible participation awareness (96.4% unaware), though tertiary education boosts compliance (OR = 6.07, p = 0.0311).
Summary Conclusion:
The enforcement of development control in Adansi Asokwa faces systemic challenges due to inadequate public awareness, weak institutional presence, and ineffective control measures. While institutional support and trust significantly drive compliance, their impact is curtailed by limited assistance and engagement opportunities. Addressing these issues requires targeted awareness campaigns, strengthened institutional capacity, and enhanced enforcement mechanisms to improve compliance and curb unauthorized development.